Databricks-Spark-Assoc Using Spark Connect Practice Question
A data engineer is using Spark Connect from a local Python environment to connect to a Databricks cluster. They attempt to use the spark.sparkContext.broadcast() method to broadcast a large lookup dictionary for use in a UDF. The code fails. What is the most likely reason for this failure?
⚠ Common exam trap
The trap here is assuming that Spark Connect supports all SparkContext APIs, when in fact it intentionally omits them to maintain decoupling.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Broadcast variables are not supported in Spark Connect because the client does not have access to the SparkContext.
Spark Connect clients do not have a SparkContext, so APIs like broadcast() that rely on it are unavailable. The client interacts with the remote Spark server through a thin client, and operations requiring direct driver access, such as creating broadcast variables, are not supported. Alternative approaches like using joins with small DataFrames should be used.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Broadcast variables are only supported when using the Scala API, not the Python API, in Spark Connect.
Why it's wrong here
This is incorrect because the limitation is not language-specific. Spark Connect does not expose SparkContext in any language, including Scala. Broadcast variables are a feature of the Spark core API that relies on the driver, which is not accessible from a Spark Connect client. Thus, the issue is architectural, not related to the programming language.
- ✗
The broadcast variable size exceeds the maximum allowed by Spark Connect, causing a failure.
Why it's wrong here
This is incorrect because the failure is not due to size limits. Spark Connect does not support broadcast variables at all because the client cannot access the SparkContext. Even a small broadcast would fail. The error occurs because the method itself is unavailable, not because of a size constraint.
- ✓
Broadcast variables are not supported in Spark Connect because the client does not have access to the SparkContext.
Why this is correct
This is correct because Spark Connect clients do not have a SparkContext; they interact with the Spark server through a remote client. The broadcast() method is part of the SparkContext API, which is not available in Spark Connect. Therefore, attempting to use spark.sparkContext.broadcast() will fail because spark.sparkContext is not defined in the Spark Connect session.
- ✗
The broadcast variable must be created using the SparkSession.broadcast() method instead.
Why it's wrong here
This is incorrect because SparkSession does not have a broadcast() method. Broadcast variables are created via SparkContext, which is not accessible in Spark Connect. There is no alternative broadcast API on SparkSession. The correct approach in Spark Connect is to avoid broadcast variables or use alternative techniques like joining with a small DataFrame.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-Spark-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-Spark-Assoc exam.